How Useful is Web Searching to Predict Presidential Polling Data
Andrew Lee
December 21, 2015
Introduction
President Obama, first elected in 2008, is said to be the first presidential candidate to have used modern social media tactics to reach out to voters, which contributed a largescale support from the usually inactive younger voter block. As we progress further into the 21st Century, the way social media impacts many facets of our lives only increases. Today, Facebook boasts 1.4 billion people online every month, and is the second largest website on Earth by traffic. Along with Twitter, Facebook has spread social movements far faster and wider than ever possible, and played a large part in movements such as Occupy Wallstreet, the Arab Spring, the LGBT Rights movement, and most recently Black Lives Matter.
With such an evolution in human behavioral patterns, I wanted to see how the availability of mass spread information affected election outcomes. Specifically, I wanted to see if I could correlate spikes in candidate search popularity with direct gains in polls.
For example, if Donald Trump stated his intention of banning all Muslims from entering the United States until immigration could implement better policies to filter out socially beneficial Muslim immigrants from Islamist fundamentalists, I would expect to see a largescale increase in Donald Trump’s Poll Ratings correlating to his virility on the internet by his voters. On the same note, if Bernie Sander’s voiced his support for the “Black Live Matter” movement by acknowledging the leaders when they interupted him in a Democratic Party Primary event, I would expect a general spike in his poll numbers corresponding to voters sympathetic to his cause over the internet.
Ideally, this would be a longterm study leading up to the 2016 Presidential Election. However, within the timeframe of this project’s due date, I was limited to only pre-election polls.
Google Trends
I would like to start by introducing Google Trends to you 
With Google Trends, users can search for a term, and Google returns you with a graphical representation of the total search querries over time, regional interest, and related searches pertaining to the original search querries.
Such a tool is highly useful for my project because it is the perfect way to figure out the total internet interest in a specific candidate, and with the related searches, I can use that to correlate specific events to specific spikes in candidate
Now, with just the graphical user interface, Google Trends is not too useful, as it requires manual user input. But I came across a packaged called gtrendsR (https://github.com/PMassicotte/gtrendsR), which is a customized api wrapper that allows users to work within R to search, and returns results as a list with a data.frame of the the graphical data, a data.frame of the regional interests, and a data.frame of the related search terms.
The following is an example of what can be done in gtrendsR
usr <- "cs125fp@gmail.com" # alternatively store as options() or env.var
psw <- "Abcd@1234"
gconnect(usr, psw) #Stored in R environment
plot(gtrends(c("Donald Trump", "Bernie Sanders", "Hillary Clinton", "Ben Carson"))) #optionally, gtrends can be saved as a list to be operated on later
As you can see, the gtrendsR package neatly packages data, and retreives it in a usable form. Now that I have a reliable source for internet search data, I needed to find a good source of polling data.
Polling Data
After looking around, I saw that Huffington Post combined the data sources from numerous polling organizations into one location.
Here are the links (http://elections.huffingtonpost.com/pollster/2016-national-gop-primary)
(http://elections.huffingtonpost.com/pollster/2016-national-democratic-primary)
Importing the Data
Importing the data was relatively easy.
sourcecsv = read.csv("http://elections.huffingtonpost.com/pollster/2016-national-gop-primary.csv", header=TRUE, stringsAsFactors=FALSE)
sourcecsv[sourcecsv == "NA"] <- NA
argDF <- sourcecsvNow here is a snapshot of the data provided. 
You can see a few things that are immediately problematic for comparing the data First off, all the sources are thrown into one object with no meaningful separation. At the same time, each polling agency used their own unique timeframe. (For example, Ipsos/Reuters uses a 4 day timeframe while Quinnipiac uses a 7 day timeframe) My first step was to extract all these different parts, and save them into separate dataframes with a unified date structure.
In order to unify the date structures, I took the first day and the last day from each entry point, and created a range of dates in between the two dates, and then populating the new days with repeated data. At the same time, I proceeded to create a three dimentional array, which represents data from January 1, 2015 to December 31, 2015 (As Rows), Candidates (as Columns), and different polling sources (as the third dimention)
Visualizing the data structure before programming it in R, I came up with something like this: 
So without further ado, here are the respective code for transforming the data for each party (GOP, and Democratic Party)
Transforming the GOP data
The following code can be found at http://andrewshinsuke.me/cs125/fixgop.r
fixday <- function(argDF) {
modDF<- data.frame(Start.Date=argDF$Start.Date, End.Date=argDF$End.Date, Trump=argDF$Trump, Carson=argDF$Carson, Rubio=argDF$Rubio, Cruz=argDF$Cruz, Bush=argDF$Bush, Rand.Paul=argDF$Rand.Paul, Christie=argDF$Christie, Fiorina=argDF$Fiorina, Kasich=argDF$Kasich, Huckabee=argDF$Huckabee, Santorum=argDF$Santorum, Graham=argDF$Graham, Pataki=argDF$Pataki, Gilmore=argDF$Gilmore, Jindal=argDF$Jindal, Perry=argDF$Perry, Walker=argDF$Walker, Undecided=argDF$Undecided, stringsAsFactors=FALSE)
modDF[1:2] <- lapply(modDF[1:2], as.Date)
returnDF <- setDT(modDF)[, list(dates=seq(Start.Date, End.Date, by = '1 day'),
Trump=Trump, Carson=Carson, Rubio=Rubio, Cruz=Cruz, Bush=Bush, Rand.Paul=Rand.Paul, Christie=Christie, Fiorina=Fiorina, Kasich=Kasich, Huckabee=Huckabee, Santorum=Santorum, Graham=Graham, Pataki=Pataki, Gilmore=Gilmore, Jindal=Jindal, Perry=Perry, Walker=Walker, Undecided=Undecided),
by = 1:nrow(modDF)][, nrow:= NULL][]
return(returnDF[order(dates),])
}
includeNA <- function(argDF) {
returnDF <- data.frame(stringsAsFactors=FALSE)
argDF <- as.data.frame(argDF, stringsAsFactors=FALSE)
dateSeq <- seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days")
count = 1
for(i in seq_along(dateSeq)){
if(any(dateSeq[i] %in% argDF$dates)==TRUE){
returnDF <- rbind(returnDF, argDF[match(dateSeq[i], argDF$dates),])
colnames(returnDF) <- colnames(argDF)
}else{
naList <- data.frame(dateSeq[i], NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, stringsAsFactors=FALSE)
colnames(naList) <- colnames(argDF)
returnDF <- rbind(returnDF, naList)
count <- count + 1}}
rownames(returnDF) <- as.character(seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days"))
return(returnDF[1:nrow(returnDF), 2:ncol(returnDF)])
}
allArrayMaker <- function(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, wpaor, lincolnpark, robertmorris, reasonrupe, wilsonpr) {
xipsos <- includeNA(ipsos)
xquinnipiac <- includeNA(quinnipiac)
xgravis <- includeNA(gravis)
xyougov <- includeNA(yougov)
xabc <- includeNA(abc)
xfox <- includeNA(fox)
xppp <- includeNA(ppp)
xbloomberg <- includeNA(bloomberg)
xnbcsm <- includeNA(nbcsm)
xmorningconsult <- includeNA(morningconsult)
xpubrel <- includeNA(pubrel)
xmcclatchy <- includeNA(mcclatchy)
xusc <- includeNA(usc)
xzogby <- includeNA(zogby)
xnbcwsj <- includeNA(nbcwsj)
xibdtipp <- includeNA(ibdtipp)
xmonmouth <- includeNA(monmouth)
xemerson <- includeNA(emerson)
xfairleigh <- includeNA(fairleigh)
xsuffolk <- includeNA(suffolk)
xcnn <- includeNA(cnn)
xwpaor <- includeNA(wpaor)
xlincolnpark <- includeNA(lincolnpark)
xrobertmorris <- includeNA(robertmorris)
xreasonrupe <- includeNA(reasonrupe)
xwilsonpr <- includeNA(wilsonpr)
returnArray <- abind(xipsos, xquinnipiac, xgravis, xyougov, xabc, xfox, xppp, xbloomberg, xnbcsm, xmorningconsult, xpubrel, xmcclatchy, xusc, xzogby, xnbcwsj, xibdtipp, xmonmouth, xemerson, xfairleigh, xsuffolk, xcnn, xwpaor, xlincolnpark, xrobertmorris, xreasonrupe, xwilsonpr, along=3)
dimnames(returnArray)[[3]] <- c("Ipsos/Reuters", "Quinnipiac", "Gravis Marketing/One America News", "YouGov/Economist", "ABC/Post", "FOX", "PPP (D)", "Bloomberg/Selzer", "NBC/SurveyMonkey", "Morning Consult", "Public Religion Research Institute", "McClatchy/Marist", "USC/LA Times/SurveyMonkey", "Zogby (Internet)", "NBC/WSJ", "IBD/TIPP", "Monmouth University", "Emerson College Polling Society", "Fairleigh Dickinson", "Suffolk/USA Today", "CNN", "Wilson Perkins Allen Opinion Research (R-Cruz)", "Lincoln Park Strategies (D)", "Robert Morris University", "Reason/Rupe", "Wilson Perkins Allen Opinion Research (R)")
return(returnArray)
}
sourcecsv = read.csv("http://elections.huffingtonpost.com/pollster/2016-national-gop-primary.csv", header=TRUE, stringsAsFactors=FALSE)
#As you can see, I am direcly linking back to the huffington post csv link, so whenever this code is run, it will be updated with the most recent information.
sourcecsv[sourcecsv == "NA"] <- NA
argDF <- sourcecsv
ipsos <- fixday(argDF[argDF$Pollster == "Ipsos/Reuters",])
quinnipiac <- fixday(argDF[argDF$Pollster == "Quinnipiac",])
gravis <- fixday(argDF[argDF$Pollster == "Gravis Marketing/One America News",])
yougov <- fixday(argDF[argDF$Pollster == "YouGov/Economist",])
abc <- fixday(argDF[argDF$Pollster == "ABC/Post",])
fox <- fixday(argDF[argDF$Pollster == "FOX",])
ppp <- fixday(argDF[argDF$Pollster == "PPP (D)",])
bloomberg <- fixday(argDF[argDF$Pollster == "Bloomberg/Selzer",])
nbcsm <- fixday(argDF[argDF$Pollster == "NBC/SurveyMonkey",])
morningconsult <- fixday(argDF[argDF$Pollster == "Morning Consult",])
pubrel <- fixday(argDF[argDF$Pollster == "Public Religion Research Institute",])
mcclatchy <- fixday(argDF[argDF$Pollster == "McClatchy/Marist",])
usc <- fixday(argDF[argDF$Pollster == "USC/LA Times/SurveyMonkey",])
zogby <- fixday(argDF[argDF$Pollster == "Zogby (Internet)",])
nbcwsj <- fixday(argDF[argDF$Pollster == "NBC/WSJ",])
ibdtipp <- fixday(argDF[argDF$Pollster == "IBD/TIPP",])
monmouth <- fixday(argDF[argDF$Pollster == "Monmouth University",])
emerson <- fixday(argDF[argDF$Pollster == "Emerson College Polling Society",])
fairleigh <- fixday(argDF[argDF$Pollster == "Fairleigh Dickinson",])
suffolk <- fixday(argDF[argDF$Pollster == "Suffolk/USA Today",])
cnn <- fixday(argDF[argDF$Pollster == "CNN",])
wpaor <- fixday(argDF[argDF$Pollster == "Wilson Perkins Allen Opinion Research (R-Cruz)",])
lincolnpark <- fixday(argDF[argDF$Pollster == "Lincoln Park Strategies (D)",])
robertmorris <- fixday(argDF[argDF$Pollster == "Robert Morris University",])
reasonrupe <- fixday(argDF[argDF$Pollster == "Reason/Rupe",])
wilsonpr <- fixday(argDF[argDF$Pollster == "Wilson Perkins Allen Opinion Research (R)",])
allGOP <- allArrayMaker(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, wpaor, lincolnpark, robertmorris, reasonrupe, wilsonpr)
save(allGOP, file="GOPArray.RData")For the Democratic Party
Here is the code for the Democratic Party’s Array It can be found at http://andrewshinsuke.me/cs125/fixdem.r
#So the only real difference between this code and that of the fixgop.r file is that there were sources such as Wilson Perkins Allen polling that did not poll for the Democratic party. At the same time, there were alot less candidates for the Democratic party (as is the case much of the time for incumbant parties)
fixday <- function(argDF) {
modDF<- data.frame(Start.Date=argDF$Start.Date, End.Date=argDF$End.Date, Clinton=argDF$Clinton, Sanders=argDF$Sanders, O.Malley=argDF$O.Malley, Biden=argDF$Biden, Chafee=argDF$Chafee, Lessig=argDF$Lessig, Webb=argDF$Webb, Undecided=argDF$Undecided, stringsAsFactors=FALSE)
modDF[1:2] <- lapply(modDF[1:2], as.Date)
returnDF <- setDT(modDF)[, list(dates=seq(Start.Date, End.Date, by = '1 day'),
Clinton=Clinton, Sanders=Sanders, O.Malley=O.Malley, Biden=Biden, Chafee=Chafee, Lessig, Webb=Webb, Undecided=Undecided),
by = 1:nrow(modDF)][, nrow:= NULL][]
return(returnDF[order(dates),])
}
includeNA <- function(argDF) {
returnDF <- data.frame(stringsAsFactors=FALSE)
argDF <- as.data.frame(argDF, stringsAsFactors=FALSE)
dateSeq <- seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days")
count = 1
for(i in seq_along(dateSeq)){
if(any(dateSeq[i] %in% argDF$dates)==TRUE){
returnDF <- rbind(returnDF, argDF[match(dateSeq[i], argDF$dates),])
colnames(returnDF) <- colnames(argDF)
}else{
naList <- data.frame(dateSeq[i], NA, NA, NA, NA, NA, NA, NA, NA, stringsAsFactors=FALSE)
colnames(naList) <- colnames(argDF)
returnDF <- rbind(returnDF, naList)
count <- count + 1}}
rownames(returnDF) <- as.character(seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days"))
return(returnDF[1:nrow(returnDF), 2:ncol(returnDF)])
}
allArrayMaker <- function(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, lincolnpark, robertmorris, reasonrupe) {
xipsos <- includeNA(ipsos)
xquinnipiac <- includeNA(quinnipiac)
xgravis <- includeNA(gravis)
xyougov <- includeNA(yougov)
xabc <- includeNA(abc)
xfox <- includeNA(fox)
xppp <- includeNA(ppp)
xbloomberg <- includeNA(bloomberg)
xnbcsm <- includeNA(nbcsm)
xmorningconsult <- includeNA(morningconsult)
xpubrel <- includeNA(pubrel)
xmcclatchy <- includeNA(mcclatchy)
xusc <- includeNA(usc)
xzogby <- includeNA(zogby)
xnbcwsj <- includeNA(nbcwsj)
xibdtipp <- includeNA(ibdtipp)
xmonmouth <- includeNA(monmouth)
xemerson <- includeNA(emerson)
xfairleigh <- includeNA(fairleigh)
xsuffolk <- includeNA(suffolk)
xcnn <- includeNA(cnn)
xlincolnpark <- includeNA(lincolnpark)
xrobertmorris <- includeNA(robertmorris)
xreasonrupe <- includeNA(reasonrupe)
returnArray <- abind(xipsos, xquinnipiac, xgravis, xyougov, xabc, xfox, xppp, xbloomberg, xnbcsm, xmorningconsult, xpubrel, xmcclatchy, xusc, xzogby, xnbcwsj, xibdtipp, xmonmouth, xemerson, xfairleigh, xsuffolk, xcnn, xlincolnpark, xrobertmorris, xreasonrupe, along=3)
dimnames(returnArray)[[3]] <- c("Ipsos/Reuters", "Quinnipiac", "Gravis Marketing/One America News", "YouGov/Economist", "ABC/Post", "FOX", "PPP (D)", "Bloomberg/Selzer", "NBC/SurveyMonkey", "Morning Consult", "Public Religion Research Institute", "McClatchy/Marist", "USC/LA Times/SurveyMonkey", "Zogby (Internet)", "NBC/WSJ", "IBD/TIPP", "Monmouth University", "Emerson College Polling Society", "Fairleigh Dickinson", "Suffolk/USA Today", "CNN", "Lincoln Park Strategies (D)", "Robert Morris University", "Reason/Rupe")
return(returnArray)
}
sourcecsv = read.csv("http://elections.huffingtonpost.com/pollster/2016-national-democratic-primary.csv", header=TRUE, stringsAsFactors=FALSE)
sourcecsv[sourcecsv == "NA"] <- NA
argDF <- sourcecsv
ipsos <- fixday(argDF[argDF$Pollster == "Ipsos/Reuters",])
quinnipiac <- fixday(argDF[argDF$Pollster == "Quinnipiac",])
gravis <- fixday(argDF[argDF$Pollster == "Gravis Marketing/One America News",])
yougov <- fixday(argDF[argDF$Pollster == "YouGov/Economist",])
abc <- fixday(argDF[argDF$Pollster == "ABC/Post",])
fox <- fixday(argDF[argDF$Pollster == "FOX",])
ppp <- fixday(argDF[argDF$Pollster == "PPP (D)",])
bloomberg <- fixday(argDF[argDF$Pollster == "Bloomberg/Selzer",])
nbcsm <- fixday(argDF[argDF$Pollster == "NBC/SurveyMonkey",])
morningconsult <- fixday(argDF[argDF$Pollster == "Morning Consult",])
pubrel <- fixday(argDF[argDF$Pollster == "Public Religion Research Institute",])
mcclatchy <- fixday(argDF[argDF$Pollster == "McClatchy/Marist",])
usc <- fixday(argDF[argDF$Pollster == "USC/LA Times/SurveyMonkey",])
zogby <- fixday(argDF[argDF$Pollster == "Zogby (Internet)",])
nbcwsj <- fixday(argDF[argDF$Pollster == "NBC/WSJ",])
ibdtipp <- fixday(argDF[argDF$Pollster == "IBD/TIPP",])
monmouth <- fixday(argDF[argDF$Pollster == "Monmouth University",])
emerson <- fixday(argDF[argDF$Pollster == "Emerson College Polling Society",])
fairleigh <- fixday(argDF[argDF$Pollster == "Fairleigh Dickinson",])
suffolk <- fixday(argDF[argDF$Pollster == "Suffolk/USA Today",])
cnn <- fixday(argDF[argDF$Pollster == "CNN",])
lincolnpark <- fixday(argDF[argDF$Pollster == "Lincoln Park Strategies (D)",])
robertmorris <- fixday(argDF[argDF$Pollster == "Robert Morris University",])
reasonrupe <- fixday(argDF[argDF$Pollster == "Reason/Rupe",])
allDEM <- allArrayMaker(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, lincolnpark, robertmorris, reasonrupe)
save(allDEM, file="DEMArray.RData")Thinking about what to do with the data
So now I created 2 Array’s with all the data I needed, in a unified form to compare with other forms or sources I might need in the future. With all these sources, I thought of the best way to find the “most accurate” picture of all the polling data, and concluded that creating a single data frame with candidates and dates, and taking the mean all the sources would be the most accurate way I could represent this data.
However, even with this organized form, in order to plot this in ggplot2 (which is designed to work with data that is organized so that each row is a different observation of an occurance, or thing, and different columns represent the differnt categories of data), whereas, my data was organized by different dates for rows, and the candidates for columns, I needed to melt the data to have the data points, a date column as a factor, a candidate column as a factor, so that I could graph it in ggplot2
The following code can be found at: http://andrewshinsuke.me/cs125/source.r
# Melting the dataframe using gather from the tidyr package
averagedGOPPoll <- as.data.frame(aaply(allGOP, 1:2, mean, na.rm=TRUE))
averagedGOPPoll$date = as.Date(rownames(averagedGOPPoll))
averagedGOPPoll.melt <- gather(averagedGOPPoll,candidate,percentage,-date)
averagedDEMPoll <- as.data.frame(aaply(allDEM, 1:2, mean, na.rm=TRUE))
averagedDEMPoll$date = as.Date(rownames(averagedDEMPoll))
averagedDEMPoll.melt <- gather(averagedDEMPoll,candidate,percentage,-date)Here is where I graph the melted data frames.
plotaverageGOPPoll <- ggplot(averagedGOPPoll.melt,aes(x=date,y=percentage,colour=candidate))+
geom_line() + ylab("Percentage out of 100") + xlab("Dates: January-December 2015") + scale_x_date(labels = date_format("%m/%d"), breaks = date_breaks("month")) + labs(title = "Plot of Averaged Polling Data for the Republicans")
ggsave(plotaverageGOPPoll, file="plotaverageGOPPoll.png")## Saving 7 x 5 in image## Warning: Removed 812 rows containing missing values (geom_path).plotaverageDEMPoll <- ggplot(averagedDEMPoll.melt,aes(x=date,y=percentage,colour=candidate))+
geom_line() + ylab("Percentage out of 100") + xlab("Dates: January-December 2015") + scale_x_date(labels = date_format("%m/%d"), breaks = date_breaks("month")) + labs(title = "Plot of Averaged Polling Data for the Democrats")
ggsave(plotaverageGOPPoll, file="plotaverageDEMPoll.png")## Saving 7 x 5 in image## Warning: Removed 812 rows containing missing values (geom_path).Visualizing the GOP Averaged Polls looks like this: 
Visualizing the Democratic Polls looks like this: 
Taking a step back to think about the data
Up to this point, I was able to create two separate data frames with the averaged data from multiple polling sources. Now it was time to use this data to find correlation with the data extracted from the gtrendsR package.
There were a few tricky things about this process. First off, Google Trends only supports querries up to five at a time. Therefore, in order to find all the data I needed, I would need to combine multiple gtrendsr querries into one data frame. Secondly, the data represented is the quantity relative to the total amount of searches for that day, whereas the data represented in the polling data is the percentage of votes each candidate has, (within the party)
This combined with my own lack of formal training in Statistics, led me to conclude that I do not have the resources to undertake a complete analysis project that would lead to any meaningful conclusions about the ability of candidates to tie themselves to a popular sentiment or popular cause.
Instead, I took took it upon myself to find a way to graph the 2 souces of data from Google Trends and the Huffington Post Aggregate data set.
Inspired by Hans Rosling’s Bubble Chart Presentation about world development in his presentation from 2007, I though it would be a great project to try to use a similar graph system to represent the internet interest and polling numbers of each candidate.
The first step was to reform my data so that I would have one data frame per party that combined the elements of both the melted pres_trendDF data frame and the averegedPoll.melt, for each party.
Reformatting the Data Frame in Bubble form for the GOP
I first started by creating a combined data frame called goppolls.
Now one of the problems for trying to create a motion graph is that there cannot be any NaN points in the data because that would make the graph dissapear. Therefore, I had to create an algorithm that replaced NA values of the Poll percentages (the Google Trends data did not come with any non-numeric values).
I did this by checking each element of the Percentages column of the goppolls to check for NA. If the value was not an NA, the value would not change, if it was an NA, then the algorithm would check if the previous number was an NA. If the previous number was not an NA, the new value would become that value. If the previous value was an NA, then, the algorithm would proceed to go through the Percentages column for the closest numeric value, and fill that in for that element.
Next, I needed to create a new column that recorded the change in percentages from one day to the next. This algorithm just went through the Percentages column and subtracted Percentage[i] from Percentage[i-1].
The following code can be found at: http://andrewshinsuke.me/cs125/makeGOPBubble.r
load(url("http://andrewshinsuke.me/cs125/avgGOP.RData"))
load(url("http://andrewshinsuke.me/cs125/pres_trendDF.melt.RData"))
gopgtrends <- pres_trendDF.melt[pres_trendDF.melt$candidate=='Trump' | pres_trendDF.melt$candidate=='Carson' | pres_trendDF.melt$candidate=='Rand',]
goppolls <- averagedGOPPoll.melt[averagedGOPPoll.melt$candidate=='Trump' | averagedGOPPoll.melt$candidate=='Carson' | averagedGOPPoll.melt$candidate=='Rand.Paul',]
gopbubbleDF <- data.frame(Date=gopgtrends$date, Candidate=gopgtrends$candidate, Searched=gopgtrends$searched, Percentage=goppolls$percentage)
#adding a polling percentage column
gopbubbleDF$Percentage[1] <- 0
#Fixing the Percentage data so that there are no more NA's
for(i in seq_along(gopbubbleDF$Percentage[1:length(gopbubbleDF$Percentage)])){
if(i > 1){
if(is.nan(gopbubbleDF$Percentage[i])){
if(!is.nan(gopbubbleDF$Percentage[i-1])){
z <- gopbubbleDF$Percentage[i-1]
}else{
for(n in gopbubbleDF$Percentage[i:length(gopbubbleDF$Percentage)]){
if(!is.na(n)) {
z <- n
break
}
}
}
gopbubbleDF$Percentage[i] <- z
}
}
}
changeInPercentage <- c(0)
for(i in seq_along(gopbubbleDF$Percentage[1:length(gopbubbleDF$Percentage)])){
if(i > 1){
z <- gopbubbleDF$Percentage[i] - gopbubbleDF$Percentage[i-1]
changeInPercentage <- append(changeInPercentage, z)}
}
gopbubbleDF <- cbind(gopbubbleDF, Change.In.Polling.Percentage=changeInPercentage)
save(gopbubbleDF, file="gopbubbledf.RData")Reformatting the Data Frame in Bubble form for the Democratic Party
The process for the democratic party was basically exactly the same source code is at: http://andrewshinsuke.me/cs125/makeDEMBubble.r
load(url("http://andrewshinsuke.me/cs125/avgDEM.RData"))
load(url("http://andrewshinsuke.me/cs125/pres_trendDF.melt.RData"))
averagedDEMPoll.melt## date candidate percentage
## 1 2015-01-01 Clinton NaN
## 2 2015-01-02 Clinton NaN
## 3 2015-01-03 Clinton NaN
## 4 2015-01-04 Clinton NaN
## 5 2015-01-05 Clinton NaN
## 6 2015-01-06 Clinton NaN
## 7 2015-01-07 Clinton NaN
## 8 2015-01-08 Clinton NaN
## 9 2015-01-09 Clinton NaN
## 10 2015-01-10 Clinton 61.0000000
## 11 2015-01-11 Clinton 61.0000000
## 12 2015-01-12 Clinton 61.0000000
## 13 2015-01-13 Clinton NaN
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## 2468 2015-10-05 Webb 1.0000000
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## 2480 2015-10-17 Webb 1.1666667
## 2481 2015-10-18 Webb 1.2500000
## 2482 2015-10-19 Webb 1.0000000
## 2483 2015-10-20 Webb NaN
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## 2485 2015-10-22 Webb NaN
## 2486 2015-10-23 Webb NaN
## 2487 2015-10-24 Webb NaN
## 2488 2015-10-25 Webb NaN
## 2489 2015-10-26 Webb NaN
## 2490 2015-10-27 Webb NaN
## 2491 2015-10-28 Webb NaN
## 2492 2015-10-29 Webb NaN
## 2493 2015-10-30 Webb NaN
## 2494 2015-10-31 Webb NaN
## 2495 2015-11-01 Webb NaN
## 2496 2015-11-02 Webb NaN
## 2497 2015-11-03 Webb NaN
## 2498 2015-11-04 Webb NaN
## 2499 2015-11-05 Webb NaN
## 2500 2015-11-06 Webb NaN
## 2501 2015-11-07 Webb NaN
## 2502 2015-11-08 Webb NaN
## 2503 2015-11-09 Webb NaN
## 2504 2015-11-10 Webb NaN
## 2505 2015-11-11 Webb NaN
## 2506 2015-11-12 Webb NaN
## 2507 2015-11-13 Webb NaN
## 2508 2015-11-14 Webb NaN
## 2509 2015-11-15 Webb NaN
## 2510 2015-11-16 Webb NaN
## 2511 2015-11-17 Webb NaN
## 2512 2015-11-18 Webb NaN
## 2513 2015-11-19 Webb NaN
## 2514 2015-11-20 Webb NaN
## 2515 2015-11-21 Webb NaN
## 2516 2015-11-22 Webb NaN
## 2517 2015-11-23 Webb NaN
## 2518 2015-11-24 Webb NaN
## 2519 2015-11-25 Webb NaN
## 2520 2015-11-26 Webb NaN
## 2521 2015-11-27 Webb NaN
## 2522 2015-11-28 Webb NaN
## 2523 2015-11-29 Webb NaN
## 2524 2015-11-30 Webb NaN
## 2525 2015-12-01 Webb NaN
## 2526 2015-12-02 Webb NaN
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## 2528 2015-12-04 Webb NaN
## 2529 2015-12-05 Webb NaN
## 2530 2015-12-06 Webb NaN
## 2531 2015-12-07 Webb NaN
## 2532 2015-12-08 Webb NaN
## 2533 2015-12-09 Webb NaN
## 2534 2015-12-10 Webb NaN
## 2535 2015-12-11 Webb NaN
## 2536 2015-12-12 Webb NaN
## 2537 2015-12-13 Webb NaN
## 2538 2015-12-14 Webb NaN
## 2539 2015-12-15 Webb NaN
## 2540 2015-12-16 Webb NaN
## 2541 2015-12-17 Webb NaN
## 2542 2015-12-18 Webb NaN
## 2543 2015-12-19 Webb NaN
## 2544 2015-12-20 Webb NaN
## 2545 2015-12-21 Webb NaN
## 2546 2015-12-22 Webb NaN
## 2547 2015-12-23 Webb NaN
## 2548 2015-12-24 Webb NaN
## 2549 2015-12-25 Webb NaN
## 2550 2015-12-26 Webb NaN
## 2551 2015-12-27 Webb NaN
## 2552 2015-12-28 Webb NaN
## 2553 2015-12-29 Webb NaN
## 2554 2015-12-30 Webb NaN
## 2555 2015-12-31 Webb NaN
## 2556 2015-01-01 Undecided NaN
## 2557 2015-01-02 Undecided NaN
## 2558 2015-01-03 Undecided NaN
## 2559 2015-01-04 Undecided NaN
## 2560 2015-01-05 Undecided NaN
## 2561 2015-01-06 Undecided NaN
## 2562 2015-01-07 Undecided NaN
## 2563 2015-01-08 Undecided NaN
## 2564 2015-01-09 Undecided NaN
## 2565 2015-01-10 Undecided 9.0000000
## 2566 2015-01-11 Undecided 9.0000000
## 2567 2015-01-12 Undecided 9.0000000
## 2568 2015-01-13 Undecided NaN
## 2569 2015-01-14 Undecided NaN
## 2570 2015-01-15 Undecided NaN
## 2571 2015-01-16 Undecided NaN
## 2572 2015-01-17 Undecided NaN
## 2573 2015-01-18 Undecided NaN
## 2574 2015-01-19 Undecided NaN
## 2575 2015-01-20 Undecided NaN
## 2576 2015-01-21 Undecided NaN
## 2577 2015-01-22 Undecided 11.0000000
## 2578 2015-01-23 Undecided 11.0000000
## 2579 2015-01-24 Undecided 11.0000000
## 2580 2015-01-25 Undecided 7.0000000
## 2581 2015-01-26 Undecided 3.0000000
## 2582 2015-01-27 Undecided 3.0000000
## 2583 2015-01-28 Undecided NaN
## 2584 2015-01-29 Undecided NaN
## 2585 2015-01-30 Undecided NaN
## 2586 2015-01-31 Undecided NaN
## 2587 2015-02-01 Undecided NaN
## 2588 2015-02-02 Undecided NaN
## 2589 2015-02-03 Undecided NaN
## 2590 2015-02-04 Undecided NaN
## 2591 2015-02-05 Undecided NaN
## 2592 2015-02-06 Undecided NaN
## 2593 2015-02-07 Undecided NaN
## 2594 2015-02-08 Undecided NaN
## 2595 2015-02-09 Undecided NaN
## 2596 2015-02-10 Undecided NaN
## 2597 2015-02-11 Undecided NaN
## 2598 2015-02-12 Undecided 3.0000000
## 2599 2015-02-13 Undecided 3.0000000
## 2600 2015-02-14 Undecided 3.0000000
## 2601 2015-02-15 Undecided 3.0000000
## 2602 2015-02-16 Undecided NaN
## 2603 2015-02-17 Undecided NaN
## 2604 2015-02-18 Undecided NaN
## 2605 2015-02-19 Undecided NaN
## 2606 2015-02-20 Undecided 10.0000000
## 2607 2015-02-21 Undecided 7.5000000
## 2608 2015-02-22 Undecided 7.5000000
## 2609 2015-02-23 Undecided 5.0000000
## 2610 2015-02-24 Undecided NaN
## 2611 2015-02-25 Undecided NaN
## 2612 2015-02-26 Undecided 15.0000000
## 2613 2015-02-27 Undecided 15.0000000
## 2614 2015-02-28 Undecided 15.0000000
## 2615 2015-03-01 Undecided 12.0000000
## 2616 2015-03-02 Undecided 12.0000000
## 2617 2015-03-03 Undecided 9.0000000
## 2618 2015-03-04 Undecided 9.0000000
## 2619 2015-03-05 Undecided NaN
## 2620 2015-03-06 Undecided NaN
## 2621 2015-03-07 Undecided 9.0000000
## 2622 2015-03-08 Undecided 9.0000000
## 2623 2015-03-09 Undecided 9.0000000
## 2624 2015-03-10 Undecided NaN
## 2625 2015-03-11 Undecided NaN
## 2626 2015-03-12 Undecided NaN
## 2627 2015-03-13 Undecided 3.0000000
## 2628 2015-03-14 Undecided 7.0000000
## 2629 2015-03-15 Undecided 7.0000000
## 2630 2015-03-16 Undecided 11.0000000
## 2631 2015-03-17 Undecided NaN
## 2632 2015-03-18 Undecided NaN
## 2633 2015-03-19 Undecided NaN
## 2634 2015-03-20 Undecided NaN
## 2635 2015-03-21 Undecided 7.0000000
## 2636 2015-03-22 Undecided 7.0000000
## 2637 2015-03-23 Undecided 7.0000000
## 2638 2015-03-24 Undecided NaN
## 2639 2015-03-25 Undecided NaN
## 2640 2015-03-26 Undecided 9.0000000
## 2641 2015-03-27 Undecided 9.0000000
## 2642 2015-03-28 Undecided 9.0000000
## 2643 2015-03-29 Undecided 7.0000000
## 2644 2015-03-30 Undecided 10.0000000
## 2645 2015-03-31 Undecided 10.0000000
## 2646 2015-04-01 Undecided 14.0000000
## 2647 2015-04-02 Undecided 14.0000000
## 2648 2015-04-03 Undecided NaN
## 2649 2015-04-04 Undecided NaN
## 2650 2015-04-05 Undecided NaN
## 2651 2015-04-06 Undecided NaN
## 2652 2015-04-07 Undecided NaN
## 2653 2015-04-08 Undecided NaN
## 2654 2015-04-09 Undecided NaN
## 2655 2015-04-10 Undecided NaN
## 2656 2015-04-11 Undecided 8.0000000
## 2657 2015-04-12 Undecided 8.0000000
## 2658 2015-04-13 Undecided 8.0000000
## 2659 2015-04-14 Undecided NaN
## 2660 2015-04-15 Undecided NaN
## 2661 2015-04-16 Undecided 11.0000000
## 2662 2015-04-17 Undecided 11.0000000
## 2663 2015-04-18 Undecided 10.3333333
## 2664 2015-04-19 Undecided 9.0000000
## 2665 2015-04-20 Undecided 10.3333333
## 2666 2015-04-21 Undecided 11.0000000
## 2667 2015-04-22 Undecided NaN
## 2668 2015-04-23 Undecided NaN
## 2669 2015-04-24 Undecided NaN
## 2670 2015-04-25 Undecided 15.0000000
## 2671 2015-04-26 Undecided 15.0000000
## 2672 2015-04-27 Undecided 15.0000000
## 2673 2015-04-28 Undecided NaN
## 2674 2015-04-29 Undecided NaN
## 2675 2015-04-30 Undecided NaN
## 2676 2015-05-01 Undecided NaN
## 2677 2015-05-02 Undecided 8.0000000
## 2678 2015-05-03 Undecided 8.0000000
## 2679 2015-05-04 Undecided 8.0000000
## 2680 2015-05-05 Undecided NaN
## 2681 2015-05-06 Undecided NaN
## 2682 2015-05-07 Undecided 11.0000000
## 2683 2015-05-08 Undecided 11.0000000
## 2684 2015-05-09 Undecided 8.3333333
## 2685 2015-05-10 Undecided 8.3333333
## 2686 2015-05-11 Undecided 7.0000000
## 2687 2015-05-12 Undecided 6.0000000
## 2688 2015-05-13 Undecided NaN
## 2689 2015-05-14 Undecided NaN
## 2690 2015-05-15 Undecided NaN
## 2691 2015-05-16 Undecided 11.0000000
## 2692 2015-05-17 Undecided 11.0000000
## 2693 2015-05-18 Undecided 11.0000000
## 2694 2015-05-19 Undecided 16.0000000
## 2695 2015-05-20 Undecided 16.0000000
## 2696 2015-05-21 Undecided 16.0000000
## 2697 2015-05-22 Undecided 16.0000000
## 2698 2015-05-23 Undecided 13.0000000
## 2699 2015-05-24 Undecided 13.0000000
## 2700 2015-05-25 Undecided 13.0000000
## 2701 2015-05-26 Undecided 16.0000000
## 2702 2015-05-27 Undecided NaN
## 2703 2015-05-28 Undecided 6.0000000
## 2704 2015-05-29 Undecided 6.0000000
## 2705 2015-05-30 Undecided 7.6666667
## 2706 2015-05-31 Undecided 7.2500000
## 2707 2015-06-01 Undecided 8.5000000
## 2708 2015-06-02 Undecided 6.0000000
## 2709 2015-06-03 Undecided NaN
## 2710 2015-06-04 Undecided NaN
## 2711 2015-06-05 Undecided NaN
## 2712 2015-06-06 Undecided 15.0000000
## 2713 2015-06-07 Undecided 15.0000000
## 2714 2015-06-08 Undecided 15.0000000
## 2715 2015-06-09 Undecided NaN
## 2716 2015-06-10 Undecided NaN
## 2717 2015-06-11 Undecided 14.0000000
## 2718 2015-06-12 Undecided 14.0000000
## 2719 2015-06-13 Undecided 11.3333333
## 2720 2015-06-14 Undecided 9.2500000
## 2721 2015-06-15 Undecided 4.5000000
## 2722 2015-06-16 Undecided 3.0000000
## 2723 2015-06-17 Undecided 3.0000000
## 2724 2015-06-18 Undecided 3.0000000
## 2725 2015-06-19 Undecided NaN
## 2726 2015-06-20 Undecided 12.0000000
## 2727 2015-06-21 Undecided 8.5000000
## 2728 2015-06-22 Undecided 8.5000000
## 2729 2015-06-23 Undecided 5.0000000
## 2730 2015-06-24 Undecided NaN
## 2731 2015-06-25 Undecided NaN
## 2732 2015-06-26 Undecided 5.0000000
## 2733 2015-06-27 Undecided 7.0000000
## 2734 2015-06-28 Undecided 7.0000000
## 2735 2015-06-29 Undecided 9.0000000
## 2736 2015-06-30 Undecided NaN
## 2737 2015-07-01 Undecided NaN
## 2738 2015-07-02 Undecided NaN
## 2739 2015-07-03 Undecided NaN
## 2740 2015-07-04 Undecided 8.0000000
## 2741 2015-07-05 Undecided 8.0000000
## 2742 2015-07-06 Undecided 8.0000000
## 2743 2015-07-07 Undecided NaN
## 2744 2015-07-08 Undecided NaN
## 2745 2015-07-09 Undecided 16.6666667
## 2746 2015-07-10 Undecided 16.6666667
## 2747 2015-07-11 Undecided 16.6666667
## 2748 2015-07-12 Undecided 16.6666667
## 2749 2015-07-13 Undecided 11.0000000
## 2750 2015-07-14 Undecided 11.0000000
## 2751 2015-07-15 Undecided 5.0000000
## 2752 2015-07-16 Undecided 6.0000000
## 2753 2015-07-17 Undecided 7.5000000
## 2754 2015-07-18 Undecided 8.3333333
## 2755 2015-07-19 Undecided 8.3333333
## 2756 2015-07-20 Undecided 10.3333333
## 2757 2015-07-21 Undecided 12.0000000
## 2758 2015-07-22 Undecided 4.0000000
## 2759 2015-07-23 Undecided 8.0000000
## 2760 2015-07-24 Undecided 8.0000000
## 2761 2015-07-25 Undecided 8.3333333
## 2762 2015-07-26 Undecided 9.6666667
## 2763 2015-07-27 Undecided 9.6666667
## 2764 2015-07-28 Undecided 9.6666667
## 2765 2015-07-29 Undecided 8.5000000
## 2766 2015-07-30 Undecided 9.3333333
## 2767 2015-07-31 Undecided 10.7500000
## 2768 2015-08-01 Undecided 10.8000000
## 2769 2015-08-02 Undecided 10.8000000
## 2770 2015-08-03 Undecided 11.3333333
## 2771 2015-08-04 Undecided 10.5000000
## 2772 2015-08-05 Undecided 11.0000000
## 2773 2015-08-06 Undecided NaN
## 2774 2015-08-07 Undecided 12.0000000
## 2775 2015-08-08 Undecided 11.0000000
## 2776 2015-08-09 Undecided 11.0000000
## 2777 2015-08-10 Undecided 10.0000000
## 2778 2015-08-11 Undecided 7.0000000
## 2779 2015-08-12 Undecided 7.0000000
## 2780 2015-08-13 Undecided 3.5000000
## 2781 2015-08-14 Undecided 10.3333333
## 2782 2015-08-15 Undecided 10.5000000
## 2783 2015-08-16 Undecided 10.5000000
## 2784 2015-08-17 Undecided 9.0000000
## 2785 2015-08-18 Undecided 9.0000000
## 2786 2015-08-19 Undecided 11.0000000
## 2787 2015-08-20 Undecided 13.0000000
## 2788 2015-08-21 Undecided 13.0000000
## 2789 2015-08-22 Undecided 11.0000000
## 2790 2015-08-23 Undecided 11.0000000
## 2791 2015-08-24 Undecided 11.0000000
## 2792 2015-08-25 Undecided 11.0000000
## 2793 2015-08-26 Undecided 9.0000000
## 2794 2015-08-27 Undecided NaN
## 2795 2015-08-28 Undecided 11.3333333
## 2796 2015-08-29 Undecided 11.0000000
## 2797 2015-08-30 Undecided 11.0000000
## 2798 2015-08-31 Undecided 9.6666667
## 2799 2015-09-01 Undecided 9.6666667
## 2800 2015-09-02 Undecided 12.0000000
## 2801 2015-09-03 Undecided NaN
## 2802 2015-09-04 Undecided 9.0000000
## 2803 2015-09-05 Undecided 8.7500000
## 2804 2015-09-06 Undecided 8.7500000
## 2805 2015-09-07 Undecided 8.8000000
## 2806 2015-09-08 Undecided 7.7500000
## 2807 2015-09-09 Undecided 9.5000000
## 2808 2015-09-10 Undecided 9.0000000
## 2809 2015-09-11 Undecided 6.6666667
## 2810 2015-09-12 Undecided 7.0000000
## 2811 2015-09-13 Undecided 7.0000000
## 2812 2015-09-14 Undecided 5.3333333
## 2813 2015-09-15 Undecided 5.3333333
## 2814 2015-09-16 Undecided 6.3333333
## 2815 2015-09-17 Undecided 7.2500000
## 2816 2015-09-18 Undecided 9.7142857
## 2817 2015-09-19 Undecided 9.8571429
## 2818 2015-09-20 Undecided 7.4285714
## 2819 2015-09-21 Undecided 6.5000000
## 2820 2015-09-22 Undecided 5.0000000
## 2821 2015-09-23 Undecided 5.6666667
## 2822 2015-09-24 Undecided 7.5000000
## 2823 2015-09-25 Undecided 7.5000000
## 2824 2015-09-26 Undecided 7.2000000
## 2825 2015-09-27 Undecided 7.2000000
## 2826 2015-09-28 Undecided 6.7500000
## 2827 2015-09-29 Undecided 4.3333333
## 2828 2015-09-30 Undecided 4.0000000
## 2829 2015-10-01 Undecided 7.0000000
## 2830 2015-10-02 Undecided 8.5000000
## 2831 2015-10-03 Undecided 8.4000000
## 2832 2015-10-04 Undecided 8.4000000
## 2833 2015-10-05 Undecided 10.3333333
## 2834 2015-10-06 Undecided 8.0000000
## 2835 2015-10-07 Undecided 8.0000000
## 2836 2015-10-08 Undecided 10.5000000
## 2837 2015-10-09 Undecided 10.5000000
## 2838 2015-10-10 Undecided 8.0000000
## 2839 2015-10-11 Undecided 8.0000000
## 2840 2015-10-12 Undecided 8.0000000
## 2841 2015-10-13 Undecided 7.5000000
## 2842 2015-10-14 Undecided 6.6666667
## 2843 2015-10-15 Undecided 7.8333333
## 2844 2015-10-16 Undecided 7.3333333
## 2845 2015-10-17 Undecided 7.5714286
## 2846 2015-10-18 Undecided 8.4000000
## 2847 2015-10-19 Undecided 10.5000000
## 2848 2015-10-20 Undecided 9.0000000
## 2849 2015-10-21 Undecided 9.0000000
## 2850 2015-10-22 Undecided 10.0000000
## 2851 2015-10-23 Undecided 8.0000000
## 2852 2015-10-24 Undecided 9.3333333
## 2853 2015-10-25 Undecided 7.7500000
## 2854 2015-10-26 Undecided 11.2500000
## 2855 2015-10-27 Undecided 8.2500000
## 2856 2015-10-28 Undecided 9.0000000
## 2857 2015-10-29 Undecided 9.8333333
## 2858 2015-10-30 Undecided 11.0000000
## 2859 2015-10-31 Undecided 10.6000000
## 2860 2015-11-01 Undecided 9.5000000
## 2861 2015-11-02 Undecided 9.4000000
## 2862 2015-11-03 Undecided 9.0000000
## 2863 2015-11-04 Undecided 6.5000000
## 2864 2015-11-05 Undecided 7.0000000
## 2865 2015-11-06 Undecided 6.3333333
## 2866 2015-11-07 Undecided 7.2500000
## 2867 2015-11-08 Undecided 7.2500000
## 2868 2015-11-09 Undecided 7.0000000
## 2869 2015-11-10 Undecided 7.5000000
## 2870 2015-11-11 Undecided 10.0000000
## 2871 2015-11-12 Undecided NaN
## 2872 2015-11-13 Undecided 9.0000000
## 2873 2015-11-14 Undecided 11.0000000
## 2874 2015-11-15 Undecided 10.2500000
## 2875 2015-11-16 Undecided 8.0000000
## 2876 2015-11-17 Undecided 7.8333333
## 2877 2015-11-18 Undecided 6.6666667
## 2878 2015-11-19 Undecided 4.0000000
## 2879 2015-11-20 Undecided 5.0000000
## 2880 2015-11-21 Undecided 6.5000000
## 2881 2015-11-22 Undecided 6.5000000
## 2882 2015-11-23 Undecided 6.6666667
## 2883 2015-11-24 Undecided 7.5000000
## 2884 2015-11-25 Undecided 7.5000000
## 2885 2015-11-26 Undecided 7.0000000
## 2886 2015-11-27 Undecided 5.0000000
## 2887 2015-11-28 Undecided 6.6666667
## 2888 2015-11-29 Undecided 6.6666667
## 2889 2015-11-30 Undecided 7.0000000
## 2890 2015-12-01 Undecided 7.0000000
## 2891 2015-12-02 Undecided 10.5000000
## 2892 2015-12-03 Undecided 11.5000000
## 2893 2015-12-04 Undecided 10.2000000
## 2894 2015-12-05 Undecided 10.4000000
## 2895 2015-12-06 Undecided 9.1666667
## 2896 2015-12-07 Undecided 7.7500000
## 2897 2015-12-08 Undecided 5.6666667
## 2898 2015-12-09 Undecided 5.6666667
## 2899 2015-12-10 Undecided 9.5000000
## 2900 2015-12-11 Undecided 10.3333333
## 2901 2015-12-12 Undecided 10.2500000
## 2902 2015-12-13 Undecided 10.2500000
## 2903 2015-12-14 Undecided 11.0000000
## 2904 2015-12-15 Undecided 11.0000000
## 2905 2015-12-16 Undecided 7.0000000
## 2906 2015-12-17 Undecided 5.5000000
## 2907 2015-12-18 Undecided NaN
## 2908 2015-12-19 Undecided NaN
## 2909 2015-12-20 Undecided NaN
## 2910 2015-12-21 Undecided NaN
## 2911 2015-12-22 Undecided NaN
## 2912 2015-12-23 Undecided NaN
## 2913 2015-12-24 Undecided NaN
## 2914 2015-12-25 Undecided NaN
## 2915 2015-12-26 Undecided NaN
## 2916 2015-12-27 Undecided NaN
## 2917 2015-12-28 Undecided NaN
## 2918 2015-12-29 Undecided NaN
## 2919 2015-12-30 Undecided NaN
## 2920 2015-12-31 Undecided NaNdemgtrends <- pres_trendDF.melt[pres_trendDF.melt$candidate=='Hillary' | pres_trendDF.melt$candidate=='Bernie',]
dempolls <- averagedDEMPoll.melt[averagedDEMPoll.melt$candidate=='Clinton' | averagedDEMPoll.melt$candidate=='Sanders',]
dembubbleDF <- data.frame(Date=demgtrends$date, Candidate=demgtrends$candidate, Searched=demgtrends$searched, Percentage=dempolls$percentage)
#adding a polling percentage column
dembubbleDF$Percentage[1] <- 0
#Fixing the Percentage data so that there are no more NA's
for(i in seq_along(dembubbleDF$Percentage[1:length(dembubbleDF$Percentage)])){
if(i > 1){
if(is.nan(dembubbleDF$Percentage[i])){
if(!is.nan(dembubbleDF$Percentage[i-1])){
z <- dembubbleDF$Percentage[i-1]
}else{
for(n in dembubbleDF$Percentage[i:length(dembubbleDF$Percentage)]){
if(!is.na(n)) {
z <- n
break
}
}
}
dembubbleDF$Percentage[i] <- z
}
}
}
changeInPercentage <- c(0)
for(i in seq_along(dembubbleDF$Percentage[1:length(dembubbleDF$Percentage)])){
if(i > 1){
z <- dembubbleDF$Percentage[i] - dembubbleDF$Percentage[i-1]
changeInPercentage <- append(changeInPercentage, z)}
}
dembubbleDF <- cbind(dembubbleDF, Change.In.Polling.Percentage=changeInPercentage)
save(dembubbleDF, file="dembubbledf.RData")Creating the Bubble Chart Graph
Now it was time to use the Bubble Data Frames to create the Bubble Charts. I proceeded to look at tutorials that detailed way to achieve similar results with Hans Rosling. One method was to look at the tool he used, which was Gapminder. However, after further research, I found out that Gapminder was bought by Google, which made it into an R packaged called googlevis. However, this used Adobe Flash technology to disply the results on a webpage, and since I wanted to do this project in R (as well as having a programmers distaste for Flash), I decided to go against this.
The second option was to produce the motion picture using the animation package. However, after reading the documentation, I could not really figure this package out. Therefore, what I ended up doing is to create 2 sets of 365 PNG images, and combining them in a non-linear video editor to create a single video for each party.
(as a note, the code can only be run through a special environment I have set up. If you want to recreate this process, there are notes in the code)
Creating the PNG’s for the Democratic Party
source code is at: http://andrewshinsuke.me/cs125/saveDEMVideo.r
##CODE WILL NOT WORK AS IS to PRODUCE THE 365 PNG'S ALTHOUGH IT WILL RUN WITHOUT A PROBLEM
##TO RUN THIS AS I RAN IT, YOU WILL NEED A IDE WITH A MACROS RECORDING SYSTEM
##FOR SUBLIME TEXT 3, THIS IS CTRL-Q TO RECORD, CTRL-Q TO STOP RECORDING, CTRL-SHIFT-Q TO ACTIVATE THE KEYSTROKES.
##THEREFORE IT IS A GIVEN THAT YOU WILL NEED SUBLIME TEXT, A PACKAGED CALLED REPL WHICH INSTALLS R AND PYTHON WORKING ENVIRONMENTS WITHIN SUBLIME, CUSTOM KEYBINDINGS FOR REPL, WHICH FOR ME, I HAVE IT SET TO COMMAND-OPT-S TO SELECTIVELY RUN SOMETHING
##STARTING RECORDING AT THE BEGINNING OF day1 <-..., OPT-SHIFT-RIGHT, COMMAND-RIGHT, COMMAND-SHIFT-LEFT, DELETE, COMMAND-UP, OPT-DOWN, DOWN, OPT-RIGHT, COMMAND-V, TYPE 'plot', OPT-RIGHT-RIGHT-RIGHT, OPT-SHIFT-LEFT, COMMAND-V, OPT-DOWN, DOWN, COMMAND-RIGHT, OPT-LEFT-LEFT-LEFT-LEFT-LEFT-LEFT, OPT-SHIFT-RIGHT, COMMANDV, OPT-DOWN, DOWN, COMMAND-SHIFT-UP, OPT-COMMAND-S, DOWN.
load(url("http://andrewshinsuke.me/cs125/dembubbledf.RData"))
#The bottom step of creating individual png's were done manually with Sublime Macro's
#In essence, I repeated replacing the day1:day365 information using an automated process that recorded my key strokes
#After getting 365 PNG Files, I put those files into a video editor and merged them into a single video
day1 <- dembubbleDF[c(1, 366),]
day2 <- dembubbleDF[c(2, 367),]
day3 <- dembubbleDF[c(3, 368),]
day4 <- dembubbleDF[c(4, 369),]
day5 <- dembubbleDF[c(5, 370),]
day6 <- dembubbleDF[c(6, 371),]
day7 <- dembubbleDF[c(7, 372),]
day8 <- dembubbleDF[c(8, 373),]
day9 <- dembubbleDF[c(9, 374),]
day10 <- dembubbleDF[c(10, 375),]
day11 <- dembubbleDF[c(11, 376),]
day12 <- dembubbleDF[c(12, 377),]
day13 <- dembubbleDF[c(13, 378),]
day14 <- dembubbleDF[c(14, 379),]
day15 <- dembubbleDF[c(15, 380),]
day16 <- dembubbleDF[c(16, 381),]
day17 <- dembubbleDF[c(17, 382),]
day18 <- dembubbleDF[c(18, 383),]
day19 <- dembubbleDF[c(19, 384),]
day20 <- dembubbleDF[c(20, 385),]
day21 <- dembubbleDF[c(21, 386),]
day22 <- dembubbleDF[c(22, 387),]
day23 <- dembubbleDF[c(23, 388),]
day24 <- dembubbleDF[c(24, 389),]
day25 <- dembubbleDF[c(25, 390),]
day26 <- dembubbleDF[c(26, 391),]
day27 <- dembubbleDF[c(27, 392),]
day28 <- dembubbleDF[c(28, 393),]
day29 <- dembubbleDF[c(29, 394),]
day30 <- dembubbleDF[c(30, 395),]
day31 <- dembubbleDF[c(31, 396),]
day32 <- dembubbleDF[c(32, 397),]
day33 <- dembubbleDF[c(33, 398),]
day34 <- dembubbleDF[c(34, 399),]
day35 <- dembubbleDF[c(35, 400),]
day36 <- dembubbleDF[c(36, 401),]
day37 <- dembubbleDF[c(37, 402),]
day38 <- dembubbleDF[c(38, 403),]
day39 <- dembubbleDF[c(39, 404),]
day40 <- dembubbleDF[c(40, 405),]
day41 <- dembubbleDF[c(41, 406),]
day42 <- dembubbleDF[c(42, 407),]
day43 <- dembubbleDF[c(43, 408),]
day44 <- dembubbleDF[c(44, 409),]
day45 <- dembubbleDF[c(45, 410),]
day46 <- dembubbleDF[c(46, 411),]
day47 <- dembubbleDF[c(47, 412),]
day48 <- dembubbleDF[c(48, 413),]
day49 <- dembubbleDF[c(49, 414),]
day50 <- dembubbleDF[c(50, 415),]
day51 <- dembubbleDF[c(51, 416),]
day52 <- dembubbleDF[c(52, 417),]
day53 <- dembubbleDF[c(53, 418),]
day54 <- dembubbleDF[c(54, 419),]
day55 <- dembubbleDF[c(55, 420),]
day56 <- dembubbleDF[c(56, 421),]
day57 <- dembubbleDF[c(57, 422),]
day58 <- dembubbleDF[c(58, 423),]
day59 <- dembubbleDF[c(59, 424),]
day60 <- dembubbleDF[c(60, 425),]
day61 <- dembubbleDF[c(61, 426),]
day62 <- dembubbleDF[c(62, 427),]
day63 <- dembubbleDF[c(63, 428),]
day64 <- dembubbleDF[c(64, 429),]
day65 <- dembubbleDF[c(65, 430),]
day66 <- dembubbleDF[c(66, 431),]
day67 <- dembubbleDF[c(67, 432),]
day68 <- dembubbleDF[c(68, 433),]
day69 <- dembubbleDF[c(69, 434),]
day70 <- dembubbleDF[c(70, 435),]
day71 <- dembubbleDF[c(71, 436),]
day72 <- dembubbleDF[c(72, 437),]
day73 <- dembubbleDF[c(73, 438),]
day74 <- dembubbleDF[c(74, 439),]
day75 <- dembubbleDF[c(75, 440),]
day76 <- dembubbleDF[c(76, 441),]
day77 <- dembubbleDF[c(77, 442),]
day78 <- dembubbleDF[c(78, 443),]
day79 <- dembubbleDF[c(79, 444),]
day80 <- dembubbleDF[c(80, 445),]
day81 <- dembubbleDF[c(81, 446),]
day82 <- dembubbleDF[c(82, 447),]
day83 <- dembubbleDF[c(83, 448),]
day84 <- dembubbleDF[c(84, 449),]
day85 <- dembubbleDF[c(85, 450),]
day86 <- dembubbleDF[c(86, 451),]
day87 <- dembubbleDF[c(87, 452),]
day88 <- dembubbleDF[c(88, 453),]
day89 <- dembubbleDF[c(89, 454),]
day90 <- dembubbleDF[c(90, 455),]
day91 <- dembubbleDF[c(91, 456),]
day92 <- dembubbleDF[c(92, 457),]
day93 <- dembubbleDF[c(93, 458),]
day94 <- dembubbleDF[c(94, 459),]
day95 <- dembubbleDF[c(95, 460),]
day96 <- dembubbleDF[c(96, 461),]
day97 <- dembubbleDF[c(97, 462),]
day98 <- dembubbleDF[c(98, 463),]
day99 <- dembubbleDF[c(99, 464),]
day100 <- dembubbleDF[c(100, 465),]
day101 <- dembubbleDF[c(101, 466),]
day102 <- dembubbleDF[c(102, 467),]
day103 <- dembubbleDF[c(103, 468),]
day104 <- dembubbleDF[c(104, 469),]
day105 <- dembubbleDF[c(105, 470),]
day106 <- dembubbleDF[c(106, 471),]
day107 <- dembubbleDF[c(107, 472),]
day108 <- dembubbleDF[c(108, 473),]
day109 <- dembubbleDF[c(109, 474),]
day110 <- dembubbleDF[c(110, 475),]
day111 <- dembubbleDF[c(111, 476),]
day112 <- dembubbleDF[c(112, 477),]
day113 <- dembubbleDF[c(113, 478),]
day114 <- dembubbleDF[c(114, 479),]
day115 <- dembubbleDF[c(115, 480),]
day116 <- dembubbleDF[c(116, 481),]
day117 <- dembubbleDF[c(117, 482),]
day118 <- dembubbleDF[c(118, 483),]
day119 <- dembubbleDF[c(119, 484),]
day120 <- dembubbleDF[c(120, 485),]
day121 <- dembubbleDF[c(121, 486),]
day122 <- dembubbleDF[c(122, 487),]
day123 <- dembubbleDF[c(123, 488),]
day124 <- dembubbleDF[c(124, 489),]
day125 <- dembubbleDF[c(125, 490),]
day126 <- dembubbleDF[c(126, 491),]
day127 <- dembubbleDF[c(127, 492),]
day128 <- dembubbleDF[c(128, 493),]
day129 <- dembubbleDF[c(129, 494),]
day130 <- dembubbleDF[c(130, 495),]
day131 <- dembubbleDF[c(131, 496),]
day132 <- dembubbleDF[c(132, 497),]
day133 <- dembubbleDF[c(133, 498),]
day134 <- dembubbleDF[c(134, 499),]
day135 <- dembubbleDF[c(135, 500),]
day136 <- dembubbleDF[c(136, 501),]
day137 <- dembubbleDF[c(137, 502),]
day138 <- dembubbleDF[c(138, 503),]
day139 <- dembubbleDF[c(139, 504),]
day140 <- dembubbleDF[c(140, 505),]
day141 <- dembubbleDF[c(141, 506),]
day142 <- dembubbleDF[c(142, 507),]
day143 <- dembubbleDF[c(143, 508),]
day144 <- dembubbleDF[c(144, 509),]
day145 <- dembubbleDF[c(145, 510),]
day146 <- dembubbleDF[c(146, 511),]
day147 <- dembubbleDF[c(147, 512),]
day148 <- dembubbleDF[c(148, 513),]
day149 <- dembubbleDF[c(149, 514),]
day150 <- dembubbleDF[c(150, 515),]
day151 <- dembubbleDF[c(151, 516),]
day152 <- dembubbleDF[c(152, 517),]
day153 <- dembubbleDF[c(153, 518),]
day154 <- dembubbleDF[c(154, 519),]
day155 <- dembubbleDF[c(155, 520),]
day156 <- dembubbleDF[c(156, 521),]
day157 <- dembubbleDF[c(157, 522),]
day158 <- dembubbleDF[c(158, 523),]
day159 <- dembubbleDF[c(159, 524),]
day160 <- dembubbleDF[c(160, 525),]
day161 <- dembubbleDF[c(161, 526),]
day162 <- dembubbleDF[c(162, 527),]
day163 <- dembubbleDF[c(163, 528),]
day164 <- dembubbleDF[c(164, 529),]
day165 <- dembubbleDF[c(165, 530),]
day166 <- dembubbleDF[c(166, 531),]
day167 <- dembubbleDF[c(167, 532),]
day168 <- dembubbleDF[c(168, 533),]
day169 <- dembubbleDF[c(169, 534),]
day170 <- dembubbleDF[c(170, 535),]
day171 <- dembubbleDF[c(171, 536),]
day172 <- dembubbleDF[c(172, 537),]
day173 <- dembubbleDF[c(173, 538),]
day174 <- dembubbleDF[c(174, 539),]
day175 <- dembubbleDF[c(175, 540),]
day176 <- dembubbleDF[c(176, 541),]
day177 <- dembubbleDF[c(177, 542),]
day178 <- dembubbleDF[c(178, 543),]
day179 <- dembubbleDF[c(179, 544),]
day180 <- dembubbleDF[c(180, 545),]
day181 <- dembubbleDF[c(181, 546),]
day182 <- dembubbleDF[c(182, 547),]
day183 <- dembubbleDF[c(183, 548),]
day184 <- dembubbleDF[c(184, 549),]
day185 <- dembubbleDF[c(185, 550),]
day186 <- dembubbleDF[c(186, 551),]
day187 <- dembubbleDF[c(187, 552),]
day188 <- dembubbleDF[c(188, 553),]
day189 <- dembubbleDF[c(189, 554),]
day190 <- dembubbleDF[c(190, 555),]
day191 <- dembubbleDF[c(191, 556),]
day192 <- dembubbleDF[c(192, 557),]
day193 <- dembubbleDF[c(193, 558),]
day194 <- dembubbleDF[c(194, 559),]
day195 <- dembubbleDF[c(195, 560),]
day196 <- dembubbleDF[c(196, 561),]
day197 <- dembubbleDF[c(197, 562),]
day198 <- dembubbleDF[c(198, 563),]
day199 <- dembubbleDF[c(199, 564),]
day200 <- dembubbleDF[c(200, 565),]
day201 <- dembubbleDF[c(201, 566),]
day202 <- dembubbleDF[c(202, 567),]
day203 <- dembubbleDF[c(203, 568),]
day204 <- dembubbleDF[c(204, 569),]
day205 <- dembubbleDF[c(205, 570),]
day206 <- dembubbleDF[c(206, 571),]
day207 <- dembubbleDF[c(207, 572),]
day208 <- dembubbleDF[c(208, 573),]
day209 <- dembubbleDF[c(209, 574),]
day210 <- dembubbleDF[c(210, 575),]
day211 <- dembubbleDF[c(211, 576),]
day212 <- dembubbleDF[c(212, 577),]
day213 <- dembubbleDF[c(213, 578),]
day214 <- dembubbleDF[c(214, 579),]
day215 <- dembubbleDF[c(215, 580),]
day216 <- dembubbleDF[c(216, 581),]
day217 <- dembubbleDF[c(217, 582),]
day218 <- dembubbleDF[c(218, 583),]
day219 <- dembubbleDF[c(219, 584),]
day220 <- dembubbleDF[c(220, 585),]
day221 <- dembubbleDF[c(221, 586),]
day222 <- dembubbleDF[c(222, 587),]
day223 <- dembubbleDF[c(223, 588),]
day224 <- dembubbleDF[c(224, 589),]
day225 <- dembubbleDF[c(225, 590),]
day226 <- dembubbleDF[c(226, 591),]
day227 <- dembubbleDF[c(227, 592),]
day228 <- dembubbleDF[c(228, 593),]
day229 <- dembubbleDF[c(229, 594),]
day230 <- dembubbleDF[c(230, 595),]
day231 <- dembubbleDF[c(231, 596),]
day232 <- dembubbleDF[c(232, 597),]
day233 <- dembubbleDF[c(233, 598),]
day234 <- dembubbleDF[c(234, 599),]
day235 <- dembubbleDF[c(235, 600),]
day236 <- dembubbleDF[c(236, 601),]
day237 <- dembubbleDF[c(237, 602),]
day238 <- dembubbleDF[c(238, 603),]
day239 <- dembubbleDF[c(239, 604),]
day240 <- dembubbleDF[c(240, 605),]
day241 <- dembubbleDF[c(241, 606),]
day242 <- dembubbleDF[c(242, 607),]
day243 <- dembubbleDF[c(243, 608),]
day244 <- dembubbleDF[c(244, 609),]
day245 <- dembubbleDF[c(245, 610),]
day246 <- dembubbleDF[c(246, 611),]
day247 <- dembubbleDF[c(247, 612),]
day248 <- dembubbleDF[c(248, 613),]
day249 <- dembubbleDF[c(249, 614),]
day250 <- dembubbleDF[c(250, 615),]
day251 <- dembubbleDF[c(251, 616),]
day252 <- dembubbleDF[c(252, 617),]
day253 <- dembubbleDF[c(253, 618),]
day254 <- dembubbleDF[c(254, 619),]
day255 <- dembubbleDF[c(255, 620),]
day256 <- dembubbleDF[c(256, 621),]
day257 <- dembubbleDF[c(257, 622),]
day258 <- dembubbleDF[c(258, 623),]
day259 <- dembubbleDF[c(259, 624),]
day260 <- dembubbleDF[c(260, 625),]
day261 <- dembubbleDF[c(261, 626),]
day262 <- dembubbleDF[c(262, 627),]
day263 <- dembubbleDF[c(263, 628),]
day264 <- dembubbleDF[c(264, 629),]
day265 <- dembubbleDF[c(265, 630),]
day266 <- dembubbleDF[c(266, 631),]
day267 <- dembubbleDF[c(267, 632),]
day268 <- dembubbleDF[c(268, 633),]
day269 <- dembubbleDF[c(269, 634),]
day270 <- dembubbleDF[c(270, 635),]
day271 <- dembubbleDF[c(271, 636),]
day272 <- dembubbleDF[c(272, 637),]
day273 <- dembubbleDF[c(273, 638),]
day274 <- dembubbleDF[c(274, 639),]
day275 <- dembubbleDF[c(275, 640),]
day276 <- dembubbleDF[c(276, 641),]
day277 <- dembubbleDF[c(277, 642),]
day278 <- dembubbleDF[c(278, 643),]
day279 <- dembubbleDF[c(279, 644),]
day280 <- dembubbleDF[c(280, 645),]
day281 <- dembubbleDF[c(281, 646),]
day282 <- dembubbleDF[c(282, 647),]
day283 <- dembubbleDF[c(283, 648),]
day284 <- dembubbleDF[c(284, 649),]
day285 <- dembubbleDF[c(285, 650),]
day286 <- dembubbleDF[c(286, 651),]
day287 <- dembubbleDF[c(287, 652),]
day288 <- dembubbleDF[c(288, 653),]
day289 <- dembubbleDF[c(289, 654),]
day290 <- dembubbleDF[c(290, 655),]
day291 <- dembubbleDF[c(291, 656),]
day292 <- dembubbleDF[c(292, 657),]
day293 <- dembubbleDF[c(293, 658),]
day294 <- dembubbleDF[c(294, 659),]
day295 <- dembubbleDF[c(295, 660),]
day296 <- dembubbleDF[c(296, 661),]
day297 <- dembubbleDF[c(297, 662),]
day298 <- dembubbleDF[c(298, 663),]
day299 <- dembubbleDF[c(299, 664),]
day300 <- dembubbleDF[c(300, 665),]
day301 <- dembubbleDF[c(301, 666),]
day302 <- dembubbleDF[c(302, 667),]
day303 <- dembubbleDF[c(303, 668),]
day304 <- dembubbleDF[c(304, 669),]
day305 <- dembubbleDF[c(305, 670),]
day306 <- dembubbleDF[c(306, 671),]
day307 <- dembubbleDF[c(307, 672),]
day308 <- dembubbleDF[c(308, 673),]
day309 <- dembubbleDF[c(309, 674),]
day310 <- dembubbleDF[c(310, 675),]
day311 <- dembubbleDF[c(311, 676),]
day312 <- dembubbleDF[c(312, 677),]
day313 <- dembubbleDF[c(313, 678),]
day314 <- dembubbleDF[c(314, 679),]
day315 <- dembubbleDF[c(315, 680),]
day316 <- dembubbleDF[c(316, 681),]
day317 <- dembubbleDF[c(317, 682),]
day318 <- dembubbleDF[c(318, 683),]
day319 <- dembubbleDF[c(319, 684),]
day320 <- dembubbleDF[c(320, 685),]
day321 <- dembubbleDF[c(321, 686),]
day322 <- dembubbleDF[c(322, 687),]
day323 <- dembubbleDF[c(323, 688),]
day324 <- dembubbleDF[c(324, 689),]
day325 <- dembubbleDF[c(325, 690),]
day326 <- dembubbleDF[c(326, 691),]
day327 <- dembubbleDF[c(327, 692),]
day328 <- dembubbleDF[c(328, 693),]
day329 <- dembubbleDF[c(329, 694),]
day330 <- dembubbleDF[c(330, 695),]
day331 <- dembubbleDF[c(331, 696),]
day332 <- dembubbleDF[c(332, 697),]
day333 <- dembubbleDF[c(333, 698),]
day334 <- dembubbleDF[c(334, 699),]
day335 <- dembubbleDF[c(335, 700),]
day336 <- dembubbleDF[c(336, 701),]
day337 <- dembubbleDF[c(337, 702),]
day338 <- dembubbleDF[c(338, 703),]
day339 <- dembubbleDF[c(339, 704),]
day340 <- dembubbleDF[c(340, 705),]
day341 <- dembubbleDF[c(341, 706),]
day342 <- dembubbleDF[c(342, 707),]
day343 <- dembubbleDF[c(343, 708),]
day344 <- dembubbleDF[c(344, 709),]
day345 <- dembubbleDF[c(345, 710),]
day346 <- dembubbleDF[c(346, 711),]
day347 <- dembubbleDF[c(347, 712),]
day348 <- dembubbleDF[c(348, 713),]
day349 <- dembubbleDF[c(349, 714),]
day350 <- dembubbleDF[c(350, 715),]
day351 <- dembubbleDF[c(351, 716),]
day352 <- dembubbleDF[c(352, 717),]
day353 <- dembubbleDF[c(353, 718),]
day354 <- dembubbleDF[c(354, 719),]
day355 <- dembubbleDF[c(355, 720),]
day356 <- dembubbleDF[c(356, 721),]
day357 <- dembubbleDF[c(357, 722),]
day358 <- dembubbleDF[c(358, 723),]
day359 <- dembubbleDF[c(359, 724),]
day360 <- dembubbleDF[c(360, 725),]
day361 <- dembubbleDF[c(361, 726),]
day362 <- dembubbleDF[c(362, 727),]
day363 <- dembubbleDF[c(363, 728),]
day364 <- dembubbleDF[c(364, 729),]
day365 <- dembubbleDF[c(365, 730),]
plotday1 <- ggplot(day1, aes(x = Percentage, y = Change.In.Polling.Percentage, size = Searched, fill=Candidate, label=Candidate)) +
labs(x="Total Percentage of Democrat Vote", y="Change in Percentage")+
geom_point(shape = 21, show_guide = FALSE) +
geom_text(size=4)+
# map z to area and make larger circles
scale_size_area(max_size = 70) +
scale_x_continuous(breaks = c(20, 40, 60, 80), limit = c(0, 100),
expand = c(0, 0)) +
scale_y_continuous(breaks = c(-40, -20, 0, 20, 40), limit = c(-60, 60),
expand = c(0, 0)) +
theme_bw() +
theme(panel.grid.major = element_blank(),
plot.title = element_text(size = rel(1.5), face = "bold", vjust = 1.5),
axis.title=element_blank(),
axis.text.x=element_text(size=10),
axis.text.y=element_text(size=10))+
ggtitle(paste(as.character(day1$Date[1]), " Democrats"))
ggsave(filename="DEMday1.png")## Saving 7 x 5 in imageCreating the PNG’s for the GOP
The same applies here. The source code is at: http://andrewshinsuke.me/cs125/saveGOPVideo.r
require(ggplot2)
load(url("http://andrewshinsuke.me/cs125/gopbubbledf.RData"))
day1 <- gopbubbleDF[c(1, 366, 731),]
day2 <- gopbubbleDF[c(2, 367, 732),]
day3 <- gopbubbleDF[c(3, 368, 733),]
day4 <- gopbubbleDF[c(4, 369, 734),]
day5 <- gopbubbleDF[c(5, 370, 735),]
day6 <- gopbubbleDF[c(6, 371, 736),]
day7 <- gopbubbleDF[c(7, 372, 737),]
day8 <- gopbubbleDF[c(8, 373, 738),]
day9 <- gopbubbleDF[c(9, 374, 739),]
day10 <- gopbubbleDF[c(10, 375, 740),]
day11 <-gopbubbleDF[c(11, 376, 741),]
day12 <-gopbubbleDF[c(12, 377, 742),]
day13 <-gopbubbleDF[c(13, 378, 743),]
day14 <-gopbubbleDF[c(14, 379, 744),]
day15 <-gopbubbleDF[c(15, 380, 745),]
day16 <-gopbubbleDF[c(16, 381, 746),]
day17 <-gopbubbleDF[c(17, 382, 747),]
day18 <-gopbubbleDF[c(18, 383, 748),]
day19 <-gopbubbleDF[c(19, 384, 749),]
day20 <-gopbubbleDF[c(20, 385, 750),]
day21 <- gopbubbleDF[c(21, 386, 751),]
day22 <- gopbubbleDF[c(22, 387, 752),]
day23 <- gopbubbleDF[c(23, 388, 753),]
day24 <- gopbubbleDF[c(24, 389, 754),]
day25 <- gopbubbleDF[c(25, 390, 755),]
day26 <- gopbubbleDF[c(26, 391, 756),]
day27 <- gopbubbleDF[c(27, 392, 757),]
day28 <- gopbubbleDF[c(28, 393, 758),]
day29 <- gopbubbleDF[c(29, 394, 759),]
day30 <- gopbubbleDF[c(30, 395, 760),]
day31 <- gopbubbleDF[c(31, 396, 761),]
day32 <- gopbubbleDF[c(32, 397, 762),]
day33 <- gopbubbleDF[c(33, 398, 763),]
day34 <- gopbubbleDF[c(34, 399, 764),]
day35 <- gopbubbleDF[c(35, 400, 765),]
day36 <- gopbubbleDF[c(36, 401, 766),]
day37 <- gopbubbleDF[c(37, 402, 767),]
day38 <- gopbubbleDF[c(38, 403, 768),]
day39 <- gopbubbleDF[c(39, 404, 769),]
day40 <- gopbubbleDF[c(40, 405, 770),]
day41 <- gopbubbleDF[c(41, 406, 771),]
day42 <- gopbubbleDF[c(42, 407, 772),]
day43 <- gopbubbleDF[c(43, 408, 773),]
day44 <- gopbubbleDF[c(44, 409, 774),]
day45 <- gopbubbleDF[c(45, 410, 775),]
day46 <- gopbubbleDF[c(46, 411, 776),]
day47 <- gopbubbleDF[c(47, 412, 777),]
day48 <- gopbubbleDF[c(48, 413, 778),]
day49 <- gopbubbleDF[c(49, 414, 779),]
day50 <- gopbubbleDF[c(50, 415, 780),]
day51 <- gopbubbleDF[c(51, 416, 781),]
day52 <- gopbubbleDF[c(52, 417, 782),]
day53 <- gopbubbleDF[c(53, 418, 783),]
day54 <- gopbubbleDF[c(54, 419, 784),]
day55 <- gopbubbleDF[c(55, 420, 785),]
day56 <- gopbubbleDF[c(56, 421, 786),]
day57 <- gopbubbleDF[c(57, 422, 787),]
day58 <- gopbubbleDF[c(58, 423, 788),]
day59 <- gopbubbleDF[c(59, 424, 789),]
day60 <- gopbubbleDF[c(60, 425, 790),]
day61 <- gopbubbleDF[c(61, 426, 791),]
day62 <- gopbubbleDF[c(62, 427, 792),]
day63 <- gopbubbleDF[c(63, 428, 793),]
day64 <- gopbubbleDF[c(64, 429, 794),]
day65 <- gopbubbleDF[c(65, 430, 795),]
day66 <- gopbubbleDF[c(66, 431, 796),]
day67 <- gopbubbleDF[c(67, 432, 797),]
day68 <- gopbubbleDF[c(68, 433, 798),]
day69 <- gopbubbleDF[c(69, 434, 799),]
day70 <- gopbubbleDF[c(70, 435, 800),]
day71 <- gopbubbleDF[c(71, 436, 801),]
day72 <- gopbubbleDF[c(72, 437, 802),]
day73 <- gopbubbleDF[c(73, 438, 803),]
day74 <- gopbubbleDF[c(74, 439, 804),]
day75 <- gopbubbleDF[c(75, 440, 805),]
day76 <- gopbubbleDF[c(76, 441, 806),]
day77 <- gopbubbleDF[c(77, 442, 807),]
day78 <- gopbubbleDF[c(78, 443, 808),]
day79 <- gopbubbleDF[c(79, 444, 809),]
day80 <- gopbubbleDF[c(80, 445, 810),]
day81 <- gopbubbleDF[c(81, 446, 811),]
day82 <- gopbubbleDF[c(82, 447, 812),]
day83 <- gopbubbleDF[c(83, 448, 813),]
day84 <- gopbubbleDF[c(84, 449, 814),]
day85 <- gopbubbleDF[c(85, 450, 815),]
day86 <- gopbubbleDF[c(86, 451, 816),]
day87 <- gopbubbleDF[c(87, 452, 817),]
day88 <- gopbubbleDF[c(88, 453, 818),]
day89 <- gopbubbleDF[c(89, 454, 819),]
day90 <- gopbubbleDF[c(90, 455, 820),]
day91 <- gopbubbleDF[c(91, 456, 821),]
day92 <- gopbubbleDF[c(92, 457, 822),]
day93 <- gopbubbleDF[c(93, 458, 823),]
day94 <- gopbubbleDF[c(94, 459, 824),]
day95 <- gopbubbleDF[c(95, 460, 825),]
day96 <- gopbubbleDF[c(96, 461, 826),]
day97 <- gopbubbleDF[c(97, 462, 827),]
day98 <- gopbubbleDF[c(98, 463, 828),]
day99 <- gopbubbleDF[c(99, 464, 829),]
day100 <- gopbubbleDF[c(100, 465, 830),]
day101 <- gopbubbleDF[c(101, 466, 831),]
day102 <- gopbubbleDF[c(102, 467, 832),]
day103 <- gopbubbleDF[c(103, 468, 833),]
day104 <- gopbubbleDF[c(104, 469, 834),]
day105 <- gopbubbleDF[c(105, 470, 835),]
day106 <- gopbubbleDF[c(106, 471, 836),]
day107 <- gopbubbleDF[c(107, 472, 837),]
day108 <- gopbubbleDF[c(108, 473, 838),]
day109 <- gopbubbleDF[c(109, 474, 839),]
day110 <- gopbubbleDF[c(110, 475, 840),]
day111 <- gopbubbleDF[c(111, 476, 841),]
day112 <- gopbubbleDF[c(112, 477, 842),]
day113 <- gopbubbleDF[c(113, 478, 843),]
day114 <- gopbubbleDF[c(114, 479, 844),]
day115 <- gopbubbleDF[c(115, 480, 845),]
day116 <- gopbubbleDF[c(116, 481, 846),]
day117 <- gopbubbleDF[c(117, 482, 847),]
day118 <- gopbubbleDF[c(118, 483, 848),]
day119 <- gopbubbleDF[c(119, 484, 849),]
day120 <- gopbubbleDF[c(120, 485, 850),]
day121 <- gopbubbleDF[c(121, 486, 851),]
day122 <- gopbubbleDF[c(122, 487, 852),]
day123 <- gopbubbleDF[c(123, 488, 853),]
day124 <- gopbubbleDF[c(124, 489, 854),]
day125 <- gopbubbleDF[c(125, 490, 855),]
day126 <- gopbubbleDF[c(126, 491, 856),]
day127 <- gopbubbleDF[c(127, 492, 857),]
day128 <- gopbubbleDF[c(128, 493, 858),]
day129 <- gopbubbleDF[c(129, 494, 859),]
day130 <- gopbubbleDF[c(130, 495, 860),]
day131 <- gopbubbleDF[c(131, 496, 861),]
day132 <- gopbubbleDF[c(132, 497, 862),]
day133 <- gopbubbleDF[c(133, 498, 863),]
day134 <- gopbubbleDF[c(134, 499, 864),]
day135 <- gopbubbleDF[c(135, 500, 865),]
day136 <- gopbubbleDF[c(136, 501, 866),]
day137 <- gopbubbleDF[c(137, 502, 867),]
day138 <- gopbubbleDF[c(138, 503, 868),]
day139 <- gopbubbleDF[c(139, 504, 869),]
day140 <- gopbubbleDF[c(140, 505, 870),]
day141 <- gopbubbleDF[c(141, 506, 871),]
day142 <- gopbubbleDF[c(142, 507, 872),]
day143 <- gopbubbleDF[c(143, 508, 873),]
day144 <- gopbubbleDF[c(144, 509, 874),]
day145 <- gopbubbleDF[c(145, 510, 875),]
day146 <- gopbubbleDF[c(146, 511, 876),]
day147 <- gopbubbleDF[c(147, 512, 877),]
day148 <- gopbubbleDF[c(148, 513, 878),]
day149 <- gopbubbleDF[c(149, 514, 879),]
day150 <- gopbubbleDF[c(150, 515, 880),]
day151 <- gopbubbleDF[c(151, 516, 881),]
day152 <- gopbubbleDF[c(152, 517, 882),]
day153 <- gopbubbleDF[c(153, 518, 883),]
day154 <- gopbubbleDF[c(154, 519, 884),]
day155 <- gopbubbleDF[c(155, 520, 885),]
day156 <- gopbubbleDF[c(156, 521, 886),]
day157 <- gopbubbleDF[c(157, 522, 887),]
day158 <- gopbubbleDF[c(158, 523, 888),]
day159 <- gopbubbleDF[c(159, 524, 889),]
day160 <- gopbubbleDF[c(160, 525, 890),]
day161 <- gopbubbleDF[c(161, 526, 891),]
day162 <- gopbubbleDF[c(162, 527, 892),]
day163 <- gopbubbleDF[c(163, 528, 893),]
day164 <- gopbubbleDF[c(164, 529, 894),]
day165 <- gopbubbleDF[c(165, 530, 895),]
day166 <- gopbubbleDF[c(166, 531, 896),]
day167 <- gopbubbleDF[c(167, 532, 897),]
day168 <- gopbubbleDF[c(168, 533, 898),]
day169 <- gopbubbleDF[c(169, 534, 899),]
day170 <- gopbubbleDF[c(170, 535, 900),]
day171 <- gopbubbleDF[c(171, 536, 901),]
day172 <- gopbubbleDF[c(172, 537, 902),]
day173 <- gopbubbleDF[c(173, 538, 903),]
day174 <- gopbubbleDF[c(174, 539, 904),]
day175 <- gopbubbleDF[c(175, 540, 905),]
day176 <- gopbubbleDF[c(176, 541, 906),]
day177 <- gopbubbleDF[c(177, 542, 907),]
day178 <- gopbubbleDF[c(178, 543, 908),]
day179 <- gopbubbleDF[c(179, 544, 909),]
day180 <- gopbubbleDF[c(180, 545, 910),]
day181 <- gopbubbleDF[c(181, 546, 911),]
day182 <- gopbubbleDF[c(182, 547, 912),]
day183 <- gopbubbleDF[c(183, 548, 913),]
day184 <- gopbubbleDF[c(184, 549, 914),]
day185 <- gopbubbleDF[c(185, 550, 915),]
day186 <- gopbubbleDF[c(186, 551, 916),]
day187 <- gopbubbleDF[c(187, 552, 917),]
day188 <- gopbubbleDF[c(188, 553, 918),]
day189 <- gopbubbleDF[c(189, 554, 919),]
day190 <- gopbubbleDF[c(190, 555, 920),]
day191 <- gopbubbleDF[c(191, 556, 921),]
day192 <- gopbubbleDF[c(192, 557, 922),]
day193 <- gopbubbleDF[c(193, 558, 923),]
day194 <- gopbubbleDF[c(194, 559, 924),]
day195 <- gopbubbleDF[c(195, 560, 925),]
day196 <- gopbubbleDF[c(196, 561, 926),]
day197 <- gopbubbleDF[c(197, 562, 927),]
day198 <- gopbubbleDF[c(198, 563, 928),]
day199 <- gopbubbleDF[c(199, 564, 929),]
day200 <- gopbubbleDF[c(200, 565, 930),]
day201 <- gopbubbleDF[c(201, 566, 931),]
day202 <- gopbubbleDF[c(202, 567, 932),]
day203 <- gopbubbleDF[c(203, 568, 933),]
day204 <- gopbubbleDF[c(204, 569, 934),]
day205 <- gopbubbleDF[c(205, 570, 935),]
day206 <- gopbubbleDF[c(206, 571, 936),]
day207 <- gopbubbleDF[c(207, 572, 937),]
day208 <- gopbubbleDF[c(208, 573, 938),]
day209 <- gopbubbleDF[c(209, 574, 939),]
day210 <- gopbubbleDF[c(210, 575, 940),]
day211 <- gopbubbleDF[c(211, 576, 941),]
day212 <- gopbubbleDF[c(212, 577, 942),]
day213 <- gopbubbleDF[c(213, 578, 943),]
day214 <- gopbubbleDF[c(214, 579, 944),]
day215 <- gopbubbleDF[c(215, 580, 945),]
day216 <- gopbubbleDF[c(216, 581, 946),]
day217 <- gopbubbleDF[c(217, 582, 947),]
day218 <- gopbubbleDF[c(218, 583, 948),]
day219 <- gopbubbleDF[c(219, 584, 949),]
day220 <- gopbubbleDF[c(220, 585, 950),]
day221 <- gopbubbleDF[c(221, 586, 951),]
day222 <- gopbubbleDF[c(222, 587, 952),]
day223 <- gopbubbleDF[c(223, 588, 953),]
day224 <- gopbubbleDF[c(224, 589, 954),]
day225 <- gopbubbleDF[c(225, 590, 955),]
day226 <- gopbubbleDF[c(226, 591, 956),]
day227 <- gopbubbleDF[c(227, 592, 957),]
day228 <- gopbubbleDF[c(228, 593, 958),]
day229 <- gopbubbleDF[c(229, 594, 959),]
day230 <- gopbubbleDF[c(230, 595, 960),]
day231 <- gopbubbleDF[c(231, 596, 961),]
day232 <- gopbubbleDF[c(232, 597, 962),]
day233 <- gopbubbleDF[c(233, 598, 963),]
day234 <- gopbubbleDF[c(234, 599, 964),]
day235 <- gopbubbleDF[c(235, 600, 965),]
day236 <- gopbubbleDF[c(236, 601, 966),]
day237 <- gopbubbleDF[c(237, 602, 967),]
day238 <- gopbubbleDF[c(238, 603, 968),]
day239 <- gopbubbleDF[c(239, 604, 969),]
day240 <- gopbubbleDF[c(240, 605, 970),]
day241 <- gopbubbleDF[c(241, 606, 971),]
day242 <- gopbubbleDF[c(242, 607, 972),]
day243 <- gopbubbleDF[c(243, 608, 973),]
day244 <- gopbubbleDF[c(244, 609, 974),]
day245 <- gopbubbleDF[c(245, 610, 975),]
day246 <- gopbubbleDF[c(246, 611, 976),]
day247 <- gopbubbleDF[c(247, 612, 977),]
day248 <- gopbubbleDF[c(248, 613, 978),]
day249 <- gopbubbleDF[c(249, 614, 979),]
day250 <- gopbubbleDF[c(250, 615, 980),]
day251 <- gopbubbleDF[c(251, 616, 981),]
day252 <- gopbubbleDF[c(252, 617, 982),]
day253 <- gopbubbleDF[c(253, 618, 983),]
day254 <- gopbubbleDF[c(254, 619, 984),]
day255 <- gopbubbleDF[c(255, 620, 985),]
day256 <- gopbubbleDF[c(256, 621, 986),]
day257 <- gopbubbleDF[c(257, 622, 987),]
day258 <- gopbubbleDF[c(258, 623, 988),]
day259 <- gopbubbleDF[c(259, 624, 989),]
day260 <- gopbubbleDF[c(260, 625, 990),]
day261 <- gopbubbleDF[c(261, 626, 991),]
day262 <- gopbubbleDF[c(262, 627, 992),]
day263 <- gopbubbleDF[c(263, 628, 993),]
day264 <- gopbubbleDF[c(264, 629, 994),]
day265 <- gopbubbleDF[c(265, 630, 995),]
day266 <- gopbubbleDF[c(266, 631, 996),]
day267 <- gopbubbleDF[c(267, 632, 997),]
day268 <- gopbubbleDF[c(268, 633, 998),]
day269 <- gopbubbleDF[c(269, 634, 999),]
day270 <- gopbubbleDF[c(270, 635, 1000),]
day271 <- gopbubbleDF[c(271, 636, 1001),]
day272 <- gopbubbleDF[c(272, 637, 1002),]
day273 <- gopbubbleDF[c(273, 638, 1003),]
day274 <- gopbubbleDF[c(274, 639, 1004),]
day275 <- gopbubbleDF[c(275, 640, 1005),]
day276 <- gopbubbleDF[c(276, 641, 1006),]
day277 <- gopbubbleDF[c(277, 642, 1007),]
day278 <- gopbubbleDF[c(278, 643, 1008),]
day279 <- gopbubbleDF[c(279, 644, 1009),]
day280 <- gopbubbleDF[c(280, 645, 1010),]
day281 <- gopbubbleDF[c(281, 646, 1011),]
day282 <- gopbubbleDF[c(282, 647, 1012),]
day283 <- gopbubbleDF[c(283, 648, 1013),]
day284 <- gopbubbleDF[c(284, 649, 1014),]
day285 <- gopbubbleDF[c(285, 650, 1015),]
day286 <- gopbubbleDF[c(286, 651, 1016),]
day287 <- gopbubbleDF[c(287, 652, 1017),]
day288 <- gopbubbleDF[c(288, 653, 1018),]
day289 <- gopbubbleDF[c(289, 654, 1019),]
day290 <- gopbubbleDF[c(290, 655, 1020),]
day291 <- gopbubbleDF[c(291, 656, 1021),]
day292 <- gopbubbleDF[c(292, 657, 1022),]
day293 <- gopbubbleDF[c(293, 658, 1023),]
day294 <- gopbubbleDF[c(294, 659, 1024),]
day295 <- gopbubbleDF[c(295, 660, 1025),]
day296 <- gopbubbleDF[c(296, 661, 1026),]
day297 <- gopbubbleDF[c(297, 662, 1027),]
day298 <- gopbubbleDF[c(298, 663, 1028),]
day299 <- gopbubbleDF[c(299, 664, 1029),]
day300 <- gopbubbleDF[c(300, 665, 1030),]
day301 <- gopbubbleDF[c(301, 666, 1031),]
day302 <- gopbubbleDF[c(302, 667, 1032),]
day303 <- gopbubbleDF[c(303, 668, 1033),]
day304 <- gopbubbleDF[c(304, 669, 1034),]
day305 <- gopbubbleDF[c(305, 670, 1035),]
day306 <- gopbubbleDF[c(306, 671, 1036),]
day307 <- gopbubbleDF[c(307, 672, 1037),]
day308 <- gopbubbleDF[c(308, 673, 1038),]
day309 <- gopbubbleDF[c(309, 674, 1039),]
day310 <- gopbubbleDF[c(310, 675, 1040),]
day311 <- gopbubbleDF[c(311, 676, 1041),]
day312 <- gopbubbleDF[c(312, 677, 1042),]
day313 <- gopbubbleDF[c(313, 678, 1043),]
day314 <- gopbubbleDF[c(314, 679, 1044),]
day315 <- gopbubbleDF[c(315, 680, 1045),]
day316 <- gopbubbleDF[c(316, 681, 1046),]
day317 <- gopbubbleDF[c(317, 682, 1047),]
day318 <- gopbubbleDF[c(318, 683, 1048),]
day319 <- gopbubbleDF[c(319, 684, 1049),]
day320 <- gopbubbleDF[c(320, 685, 1050),]
day321 <- gopbubbleDF[c(321, 686, 1051),]
day322 <- gopbubbleDF[c(322, 687, 1052),]
day323 <- gopbubbleDF[c(323, 688, 1053),]
day324 <- gopbubbleDF[c(324, 689, 1054),]
day325 <- gopbubbleDF[c(325, 690, 1055),]
day326 <- gopbubbleDF[c(326, 691, 1056),]
day327 <- gopbubbleDF[c(327, 692, 1057),]
day328 <- gopbubbleDF[c(328, 693, 1058),]
day329 <- gopbubbleDF[c(329, 694, 1059),]
day330 <- gopbubbleDF[c(330, 695, 1060),]
day331 <- gopbubbleDF[c(331, 696, 1061),]
day332 <- gopbubbleDF[c(332, 697, 1062),]
day333 <- gopbubbleDF[c(333, 698, 1063),]
day334 <- gopbubbleDF[c(334, 699, 1064),]
day335 <- gopbubbleDF[c(335, 700, 1065),]
day336 <- gopbubbleDF[c(336, 701, 1066),]
day337 <- gopbubbleDF[c(337, 702, 1067),]
day338 <- gopbubbleDF[c(338, 703, 1068),]
day339 <- gopbubbleDF[c(339, 704, 1069),]
day340 <- gopbubbleDF[c(340, 705, 1070),]
day341 <- gopbubbleDF[c(341, 706, 1071),]
day342 <- gopbubbleDF[c(342, 707, 1072),]
day343 <- gopbubbleDF[c(343, 708, 1073),]
day344 <- gopbubbleDF[c(344, 709, 1074),]
day345 <- gopbubbleDF[c(345, 710, 1075),]
day346 <- gopbubbleDF[c(346, 711, 1076),]
day347 <- gopbubbleDF[c(347, 712, 1077),]
day348 <- gopbubbleDF[c(348, 713, 1078),]
day349 <- gopbubbleDF[c(349, 714, 1079),]
day350 <- gopbubbleDF[c(350, 715, 1080),]
day351 <- gopbubbleDF[c(351, 716, 1081),]
day352 <- gopbubbleDF[c(352, 717, 1082),]
day353 <- gopbubbleDF[c(353, 718, 1083),]
day354 <- gopbubbleDF[c(354, 719, 1084),]
day355 <- gopbubbleDF[c(355, 720, 1085),]
day356 <- gopbubbleDF[c(356, 721, 1086),]
day357 <- gopbubbleDF[c(357, 722, 1087),]
day358 <- gopbubbleDF[c(358, 723, 1088),]
day359 <- gopbubbleDF[c(359, 724, 1089),]
day360 <- gopbubbleDF[c(360, 725, 1090),]
day361 <- gopbubbleDF[c(361, 726, 1091),]
day362 <- gopbubbleDF[c(362, 727, 1092),]
day363 <- gopbubbleDF[c(363, 728, 1093),]
day364 <- gopbubbleDF[c(364, 729, 1094),]
day365 <- gopbubbleDF[c(365, 730, 1095),]
day1plot <- ggplot(day1, aes(x = Percentage, y = Change.In.Polling.Percentage, size = Searched, fill=Candidate, label=Candidate)) +
labs(x="Total Percentage of GOP Vote", y="Change in Percentage")+
geom_point(shape = 21, show_guide = FALSE) +
geom_text(size=4)+
# map z to area and make larger circles
scale_size_area(max_size = 70) +
scale_x_continuous(breaks = c(20, 40, 60, 80), limit = c(0, 100),
expand = c(0, 0)) +
scale_y_continuous(breaks = c(-40, -20, 0, 20, 40), limit = c(-60, 60),
expand = c(0, 0)) +
theme_bw() +
theme(panel.grid.major = element_blank(),
plot.title = element_text(size = rel(1.5), face = "bold", vjust = 1.5),
axis.title=element_blank(),
axis.text.x=element_text(size=10),
axis.text.y=element_text(size=10))+
ggtitle(paste(as.character(day1$Date[1]), " GOP"))
ggsave(filename="GOPday1.png")## Saving 7 x 5 in imageCreating the Videos
Now that I have two sets of 365 individual PNG image files representing each day of 2015, I needed a way to combine this. I imported the videos into my video editor, Final Cut, and set each image to show for 5 frames, then exported the video as an M4V video.
If you want to recreate the process yourself, I have the zip files of all the PNG’s for each party saved as ZIP files, which can be downloaded here:
for the GOP: http://andrewshinsuke.me/cs125/GOP.zip for the Democrats: http://andrewshinsuke.me/cs125/Democrat.zip
Here are the videos Republican Party
Democratic Party
Conclusion
Although I could not make any definative judgments about how politicians can tie themselves to popular (or unpopular) causes, through these motion graphs, we can make some inferences.
First, we can observe Bernie Sanders rise to fame over Hillary Clinton. As he tied himself with issues popular with the younger generation such as free college tuition, Black Lives Matter, and his stance against the War on Drugs, you can see him becoming more and more popular in Google search indexes.
Contrasting that with Donald Trump, his often controversial comments have given him large notoriety on the internet, and can be attributed to his dominance in the GOP polls.
Although I have stopped here, I believe much more can be done with the data I have formed. For example, using the “related searches” data frame that gets returned in the gtrends function, someone could potentially create an algorithm that filters out the irrelevant searches, and searches for specific contexts the candidate was involved in within a given timespan. With that data, we would really be able to see if successful positioning as pro or con for a viral social media movement can affeect a politicians success.
At the same time, the three dimentional array I created from the Polling data from Huffington Post can be used in a study that compares different media outlets, and how their own perceived political leanings can have (or not have) an affect on their given poll ratings.